Indoor localization of a mobile robot has received much attention with increasing of using indoor mobile robots. In particular, localization method of the mobile robot using a laser range finder is widely researched and used owing to their high accuracy and long range of sensing.
In localization method using the laser range finder, pose of the mobile robot is assumed by sensing a natural landmark or an artificial landmark in indoor environment. A localization method based on the natural landmark uses distance or reflection intensity measured from objects in a driving environment.
On the other hand, in a localization method based on the artificial landmark, a reflector which has reflection intensity different from surrounding objects is installed around the driving environment, the pose of the mobile robot is estimated by sensing the reflector through the laser range finder. However, it is more efficient that the natural landmark is fully used, excluding environment where installation of the artificial landmark is inevitable, because the localization method based on the artificial landmark need to considerable expense and effort for installing the artificial landmarks.
The serious problem that is faced when the localization method based on the natural landmark is used is an error of a measured distance which is measured by the laser range finder. The error of the measured distance is caused by unknown obstacles or a refection property of an environment of the localization.
The localization problems due to the unknown obstacles can be solved by a design of an observation model which is robust to the partial distance error. For example, in a paper of Moon, etc. Observation Likelihood Model Design and Failure Recovery Scheme toward Reliable Localization of Mobile Robots (International Journal of Advanced Robotic Systems, vol. 7, no. 4, pp. 113-122, 2010.), a scan matching method using a difference of area between a scan image and a reference image was suggested.
Herein, the reference image is the laser image which is predicted in a potential pose of the mobile robot based on map information. The method suggested in the paper is robust to the partial distance error because the method uses not a probability multiplication according to the distance errors of each laser beam but a probability model using the difference of the area of total scan images.
A beam model was suggested in a paper of S. Thrun, D. Fox and W. Burgard  Probabilistic Robotics (The MIT Press, 2005.). The beam model dealt with the localization problem due to the unknown obstacles by statistically adding with possibility of occurrence of the error due to the adjacent obstacles.
However, the method which is suggested in the above-mentioned papers has a limit that only the partial distance error due to the unknown obstacles is solved. If the distance errors are occurred in the large number of the direction due to the reflective characteristic of the localization environment, the method which is suggested in the above-mentioned paper cannot assure itself of the performance.
Meanwhile, the typical environment which causes the distance error by the reflective characteristic of the environment is indoor environment which is surrounded with glass walls. The measured value of the laser range finder reflected from the general object is a distance which is measured by a diffuse reflection from an object what the laser beam reaches firstly.
However, as shown in FIG. 1, when the laser beam from the laser range finder reaches the transparent object such as the glass, the measured value of the laser range finder is changed depending on various reflection phenomena such as a diffuse reflection, a specular reflection, and a penetration, etc. in the glass wall. It is difficult that the reference image which is compared with the scan image for the scan matching is determined because of such the refection property.
Actually, the localization methods which consider the reflective characteristic of the laser range finder in the driving environment have been widely studied. For example, a paper of M. Bennewitz Utilizing Reflection Properties of Surfaces to Improve Mobile Robot Localization (IEEE International Conference on Robotics & Automation, 2009.) deals with the localization problems which are occurred when the low-cost laser range finder cannot detect the object with low reflectivity. However, the reflective characteristic of the glass wall is more complicated than the object with low reflectivity.
In a paper of S. Yang  On Solving Mirror Reflection in LIDAR Sensing (IEEE/ASME TRANSACTIONS ON MECHATRONICS, vol. 16, no. 2, pp. 255-265, 2011., a method for detecting and tracking a position of a mirror through analyzing the measured distance values is disclosed. However, the method has limitation that various reflections which can be occurred in the glass wall not in the mirror don't be considered.
Also, in a paper of M. Awais Improved Laser-based Navigation for Mobile Robots (International Conference on Advanced Robotics, 2009.), the technique for statistically predicting the type of the sensor's output according to the distance and the incidence angle of the beam incident toward the direction of the glass wall based on the light characteristics is disclosed. Even though the method disclosed in the paper of M. Awais uses the light characteristics of the laser beam, it is difficult to predict the type of the scan distance which is occurred by the actual glass wall.